Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations366
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.3 KiB
Average record size in memory76.4 B

Variable types

DateTime2
Numeric10
Categorical1

Alerts

close is highly overall correlated with high and 3 other fieldsHigh correlation
high is highly overall correlated with close and 4 other fieldsHigh correlation
low is highly overall correlated with close and 2 other fieldsHigh correlation
month is highly overall correlated with yearHigh correlation
num_trades is highly overall correlated with quote_asset_volume and 3 other fieldsHigh correlation
open is highly overall correlated with close and 2 other fieldsHigh correlation
quote_asset_volume is highly overall correlated with high and 4 other fieldsHigh correlation
tb_base_asset_volume is highly overall correlated with num_trades and 3 other fieldsHigh correlation
tb_quote_asset_volume is highly overall correlated with close and 5 other fieldsHigh correlation
volume is highly overall correlated with num_trades and 3 other fieldsHigh correlation
year is highly overall correlated with monthHigh correlation
open_time has unique values Unique
volume has unique values Unique
close_time has unique values Unique
quote_asset_volume has unique values Unique
num_trades has unique values Unique
tb_base_asset_volume has unique values Unique
tb_quote_asset_volume has unique values Unique

Reproduction

Analysis started2025-08-30 03:19:56.106488
Analysis finished2025-08-30 03:20:02.440037
Duration6.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

open_time
Date

Unique 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2024-07-31 00:00:00
Maximum2025-07-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-30T00:20:02.491353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:02.684592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open
Real number (ℝ)

High correlation 

Distinct354
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.75279
Minimum105.4
Maximum261.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-08-30T00:20:02.764944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum105.4
5-th percentile126.1075
Q1143.2325
median157.655
Q3189.4975
95-th percentile237.63
Maximum261.97
Range156.57001
Interquartile range (IQR)46.264999

Descriptive statistics

Standard deviation35.359009
Coefficient of variation (CV)0.20953141
Kurtosis-0.25684366
Mean168.75279
Median Absolute Deviation (MAD)20.175003
Skewness0.77684009
Sum61763.52
Variance1250.2595
MonotonicityNot monotonic
2025-08-30T00:20:02.845108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176.6300049 2
 
0.5%
167.3999939 2
 
0.5%
147.9700012 2
 
0.5%
139.0399933 2
 
0.5%
153.2700043 2
 
0.5%
128.5 2
 
0.5%
144.2799988 2
 
0.5%
166.1600037 2
 
0.5%
200.4700012 2
 
0.5%
168.9400024 2
 
0.5%
Other values (344) 346
94.5%
ValueCountFrequency (%)
105.4000015 1
0.3%
105.9000015 1
0.3%
106.9800034 1
0.3%
112.8000031 1
0.3%
117.1699982 1
0.3%
117.4199982 1
0.3%
118.3199997 1
0.3%
119.0500031 1
0.3%
120.3300018 1
0.3%
121.4100037 1
0.3%
ValueCountFrequency (%)
261.9700012 1
0.3%
257.3699951 1
0.3%
256.8999939 1
0.3%
256.3999939 1
0.3%
256.3800049 1
0.3%
254.8999939 1
0.3%
253.3600006 1
0.3%
253.1199951 1
0.3%
252.7400055 1
0.3%
252.3999939 1
0.3%

high
Real number (ℝ)

High correlation 

Distinct358
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.55628
Minimum112.58
Maximum295.82999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-08-30T00:20:02.923809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum112.58
5-th percentile130.8125
Q1147.33001
median162.92
Q3197.8075
95-th percentile245.37
Maximum295.82999
Range183.24998
Interquartile range (IQR)50.477493

Descriptive statistics

Standard deviation37.118427
Coefficient of variation (CV)0.21264446
Kurtosis-0.054951057
Mean174.55628
Median Absolute Deviation (MAD)20.834999
Skewness0.86444283
Sum63887.6
Variance1377.7776
MonotonicityNot monotonic
2025-08-30T00:20:03.005632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162 2
 
0.5%
174.3000031 2
 
0.5%
134.4499969 2
 
0.5%
139.8000031 2
 
0.5%
152.8999939 2
 
0.5%
178.3300018 2
 
0.5%
148.6999969 2
 
0.5%
144.8800049 2
 
0.5%
185.1000061 1
 
0.3%
172.9100037 1
 
0.3%
Other values (348) 348
95.1%
ValueCountFrequency (%)
112.5800018 1
0.3%
113 1
0.3%
119.3000031 1
0.3%
120.9400024 1
0.3%
121 1
0.3%
121.1299973 1
0.3%
122.6500015 1
0.3%
122.8499985 1
0.3%
124 1
0.3%
127.0800018 1
0.3%
ValueCountFrequency (%)
295.8299866 1
0.3%
273.2900085 1
0.3%
272.0299988 1
0.3%
270.6700134 1
0.3%
270.1799927 1
0.3%
264.3900146 1
0.3%
264 1
0.3%
260.7799988 1
0.3%
260.1600037 1
0.3%
260.0599976 1
0.3%

low
Real number (ℝ)

High correlation 

Distinct357
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.83607
Minimum95.260002
Maximum252.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-08-30T00:20:03.084123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95.260002
5-th percentile122.62
Q1138.63
median152.97
Q3184.47
95-th percentile230.7825
Maximum252.69
Range157.42999
Interquartile range (IQR)45.84

Descriptive statistics

Standard deviation33.83812
Coefficient of variation (CV)0.20780482
Kurtosis-0.24993342
Mean162.83607
Median Absolute Deviation (MAD)20.259995
Skewness0.73298782
Sum59598
Variance1145.0184
MonotonicityNot monotonic
2025-08-30T00:20:03.164900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185.8399963 2
 
0.5%
143.5 2
 
0.5%
178.3000031 2
 
0.5%
150.3000031 2
 
0.5%
132.0399933 2
 
0.5%
125.5500031 2
 
0.5%
173.2299957 2
 
0.5%
141.1999969 2
 
0.5%
142.9199982 2
 
0.5%
171.4400024 1
 
0.3%
Other values (347) 347
94.8%
ValueCountFrequency (%)
95.26000214 1
0.3%
101.2600021 1
0.3%
102.9599991 1
0.3%
103.8099976 1
0.3%
108.2099991 1
0.3%
110 1
0.3%
112 1
0.3%
112.1600037 1
0.3%
112.2399979 1
0.3%
113.25 1
0.3%
ValueCountFrequency (%)
252.6900024 1
0.3%
251.4700012 1
0.3%
248.4299927 1
0.3%
247.1799927 1
0.3%
246.3300018 1
0.3%
242.1900024 1
0.3%
240.9299927 1
0.3%
240.3200073 1
0.3%
237.5 1
0.3%
236.6799927 1
0.3%

close
Real number (ℝ)

High correlation 

Distinct353
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.73331
Minimum105.4
Maximum261.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-08-30T00:20:03.241855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum105.4
5-th percentile126.115
Q1143.225
median157.66
Q3189.50499
95-th percentile237.62251
Maximum261.97
Range156.57001
Interquartile range (IQR)46.279991

Descriptive statistics

Standard deviation35.355392
Coefficient of variation (CV)0.20953417
Kurtosis-0.25399944
Mean168.73331
Median Absolute Deviation (MAD)20.045006
Skewness0.77868044
Sum61756.39
Variance1250.0037
MonotonicityNot monotonic
2025-08-30T00:20:03.321121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166.1699982 2
 
0.5%
144.2899933 2
 
0.5%
143.9499969 2
 
0.5%
194.4799957 2
 
0.5%
144.7899933 2
 
0.5%
153.2599945 2
 
0.5%
182.8699951 2
 
0.5%
147.9799957 2
 
0.5%
167.3999939 2
 
0.5%
200.4700012 2
 
0.5%
Other values (343) 346
94.5%
ValueCountFrequency (%)
105.4000015 1
0.3%
105.9100037 1
0.3%
106.9899979 1
0.3%
112.8099976 1
0.3%
117.1699982 1
0.3%
117.4199982 1
0.3%
118.3199997 1
0.3%
119.0500031 1
0.3%
120.3199997 1
0.3%
121.4100037 1
0.3%
ValueCountFrequency (%)
261.9700012 1
0.3%
257.3599854 1
0.3%
256.8999939 1
0.3%
256.4100037 1
0.3%
256.3699951 1
0.3%
254.8899994 1
0.3%
253.3699951 1
0.3%
253.1100006 1
0.3%
252.7299957 1
0.3%
252.4199982 1
0.3%

volume
Real number (ℝ)

High correlation  Unique 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4083361.5
Minimum1173963.9
Maximum25051668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-08-30T00:20:03.400241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1173963.9
5-th percentile1605794.8
Q12506434.6
median3480884.6
Q34611960.4
95-th percentile8625705.2
Maximum25051668
Range23877704
Interquartile range (IQR)2105525.8

Descriptive statistics

Standard deviation2786235.2
Coefficient of variation (CV)0.68233862
Kurtosis19.673252
Mean4083361.5
Median Absolute Deviation (MAD)1016814
Skewness3.5993457
Sum1.4945103 × 109
Variance7.7631062 × 1012
MonotonicityNot monotonic
2025-08-30T00:20:03.478975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2624841.75 1
 
0.3%
3250862.5 1
 
0.3%
5523497.5 1
 
0.3%
5553018 1
 
0.3%
5444954.5 1
 
0.3%
7037715.5 1
 
0.3%
24267558 1
 
0.3%
8362643.5 1
 
0.3%
8372307.5 1
 
0.3%
7303134 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
1173963.875 1
0.3%
1230210.75 1
0.3%
1330917.125 1
0.3%
1347679.5 1
0.3%
1396833.25 1
0.3%
1406583.5 1
0.3%
1408204.625 1
0.3%
1409071.375 1
0.3%
1475217.875 1
0.3%
1488310.75 1
0.3%
ValueCountFrequency (%)
25051668 1
0.3%
24267558 1
0.3%
19971982 1
0.3%
16879576 1
0.3%
13474640 1
0.3%
12836970 1
0.3%
12630665 1
0.3%
11747010 1
0.3%
11096202 1
0.3%
10834053 1
0.3%

close_time
Date

Unique 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2024-07-31 23:59:59.999000
Maximum2025-07-31 23:59:59.999000
Invalid dates0
Invalid dates (%)0.0%
2025-08-30T00:20:03.551438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:03.629609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

quote_asset_volume
Real number (ℝ)

High correlation  Unique 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1134514 × 108
Minimum1.6833309 × 108
Maximum6.7339586 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-08-30T00:20:03.709465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.6833309 × 108
5-th percentile2.484204 × 108
Q13.9120039 × 108
median5.570246 × 108
Q37.8564669 × 108
95-th percentile1.7208719 × 109
Maximum6.7339586 × 109
Range6.5656255 × 109
Interquartile range (IQR)3.9444629 × 108

Descriptive statistics

Standard deviation6.062956 × 108
Coefficient of variation (CV)0.85232267
Kurtosis35.023708
Mean7.1134514 × 108
Median Absolute Deviation (MAD)1.8857667 × 108
Skewness4.711302
Sum2.6035232 × 1011
Variance3.6759435 × 1017
MonotonicityNot monotonic
2025-08-30T00:20:03.784540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
466405400.7 1
 
0.3%
582939536.2 1
 
0.3%
917091024.2 1
 
0.3%
880989082.7 1
 
0.3%
803132864 1
 
0.3%
979026258 1
 
0.3%
3003271601 1
 
0.3%
1184669505 1
 
0.3%
1249654525 1
 
0.3%
1137205546 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
168333087.1 1
0.3%
173152977.6 1
0.3%
192268912.5 1
0.3%
194061127 1
0.3%
197164215.7 1
0.3%
203954747.2 1
0.3%
207747277.7 1
0.3%
211080413.8 1
0.3%
212333839.8 1
0.3%
213707959.9 1
0.3%
ValueCountFrequency (%)
6733958632 1
0.3%
4849480491 1
0.3%
4215741223 1
0.3%
3003271601 1
0.3%
2538322103 1
0.3%
2287313944 1
0.3%
2212052717 1
0.3%
2120001679 1
0.3%
2058026956 1
0.3%
1960587172 1
0.3%

num_trades
Real number (ℝ)

High correlation  Unique 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2233474.3
Minimum573529
Maximum16728916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-08-30T00:20:03.859145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum573529
5-th percentile845927.75
Q11325035
median1910455.5
Q32624416.2
95-th percentile4735614.5
Maximum16728916
Range16155387
Interquartile range (IQR)1299381.2

Descriptive statistics

Standard deviation1585168.2
Coefficient of variation (CV)0.70973198
Kurtosis27.930528
Mean2233474.3
Median Absolute Deviation (MAD)630992.5
Skewness4.1863688
Sum8.1745161 × 108
Variance2.5127581 × 1012
MonotonicityNot monotonic
2025-08-30T00:20:03.938115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1927344 1
 
0.3%
1245080 1
 
0.3%
1849971 1
 
0.3%
2396392 1
 
0.3%
1940755 1
 
0.3%
2391187 1
 
0.3%
7092579 1
 
0.3%
3567599 1
 
0.3%
3639741 1
 
0.3%
3235989 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
573529 1
0.3%
587595 1
0.3%
591531 1
0.3%
647642 1
0.3%
678270 1
0.3%
704766 1
0.3%
706271 1
0.3%
706885 1
0.3%
710458 1
0.3%
714484 1
0.3%
ValueCountFrequency (%)
16728916 1
0.3%
13411844 1
0.3%
10116876 1
0.3%
9712182 1
0.3%
7092579 1
0.3%
6699907 1
0.3%
6490443 1
0.3%
6265437 1
0.3%
6188432 1
0.3%
6181509 1
0.3%

tb_base_asset_volume
Real number (ℝ)

High correlation  Unique 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023838
Minimum569246.62
Maximum12553970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-08-30T00:20:04.015239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum569246.62
5-th percentile787516.78
Q11221666.9
median1715961.9
Q32318564.7
95-th percentile4310875.6
Maximum12553970
Range11984723
Interquartile range (IQR)1096897.8

Descriptive statistics

Standard deviation1393526.9
Coefficient of variation (CV)0.68855653
Kurtosis19.539248
Mean2023838
Median Absolute Deviation (MAD)533987.06
Skewness3.5963972
Sum7.4072471 × 108
Variance1.9419173 × 1012
MonotonicityNot monotonic
2025-08-30T00:20:04.212908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1277197.625 1
 
0.3%
1612652.25 1
 
0.3%
2587951.75 1
 
0.3%
2668247 1
 
0.3%
2781021.25 1
 
0.3%
3596284 1
 
0.3%
11769163 1
 
0.3%
4250033.5 1
 
0.3%
4172450.5 1
 
0.3%
3683641 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
569246.625 1
0.3%
621349.3125 1
0.3%
658168.5625 1
0.3%
677588.5625 1
0.3%
689635.375 1
0.3%
698017.9375 1
0.3%
701765.125 1
0.3%
706661.875 1
0.3%
712534.25 1
0.3%
722864.25 1
0.3%
ValueCountFrequency (%)
12553970 1
0.3%
11769163 1
0.3%
10428787 1
0.3%
8415721 1
0.3%
7001626 1
0.3%
6239167.5 1
0.3%
6133897.5 1
0.3%
5733721.5 1
0.3%
5386859.5 1
0.3%
5308196.5 1
0.3%

tb_quote_asset_volume
Real number (ℝ)

High correlation  Unique 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5321539 × 108
Minimum83942563
Maximum3.3776738 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-08-30T00:20:04.287589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum83942563
5-th percentile1.1901977 × 108
Q11.9313638 × 108
median2.7361837 × 108
Q33.9153305 × 108
95-th percentile8.4254421 × 108
Maximum3.3776738 × 109
Range3.2937313 × 109
Interquartile range (IQR)1.9839667 × 108

Descriptive statistics

Standard deviation3.0542596 × 108
Coefficient of variation (CV)0.86470174
Kurtosis35.617137
Mean3.5321539 × 108
Median Absolute Deviation (MAD)93713044
Skewness4.770314
Sum1.2927683 × 1011
Variance9.328502 × 1016
MonotonicityNot monotonic
2025-08-30T00:20:04.363151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226987071.8 1
 
0.3%
289616268.2 1
 
0.3%
429763487.3 1
 
0.3%
423266423.2 1
 
0.3%
410572234.6 1
 
0.3%
500464080 1
 
0.3%
1457361213 1
 
0.3%
601986157.7 1
 
0.3%
622749862.2 1
 
0.3%
573962877.7 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
83942562.7 1
0.3%
85020899.85 1
0.3%
93641938.65 1
0.3%
94934406.88 1
0.3%
96316133.82 1
0.3%
103158289.3 1
0.3%
103315785.3 1
0.3%
103646715.4 1
0.3%
103688789.4 1
0.3%
106472323.6 1
0.3%
ValueCountFrequency (%)
3377673849 1
0.3%
2531314978 1
0.3%
2101081767 1
0.3%
1457361213 1
0.3%
1235396564 1
0.3%
1152127678 1
0.3%
1124569870 1
0.3%
1114450473 1
0.3%
1006642368 1
0.3%
1001610078 1
0.3%

year
Categorical

High correlation 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2025
212 
2024
154 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1464
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2025 212
57.9%
2024 154
42.1%

Length

2025-08-30T00:20:04.432796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T00:20:04.475185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025 212
57.9%
2024 154
42.1%

Most occurring characters

ValueCountFrequency (%)
2 732
50.0%
0 366
25.0%
5 212
 
14.5%
4 154
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 732
50.0%
0 366
25.0%
5 212
 
14.5%
4 154
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 732
50.0%
0 366
25.0%
5 212
 
14.5%
4 154
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 732
50.0%
0 366
25.0%
5 212
 
14.5%
4 154
 
10.5%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5273224
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-08-30T00:20:04.514588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39.75
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.4479403
Coefficient of variation (CV)0.52823196
Kurtosis-1.2023215
Mean6.5273224
Median Absolute Deviation (MAD)3
Skewness-0.011638844
Sum2389
Variance11.888293
MonotonicityNot monotonic
2025-08-30T00:20:04.566487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 32
8.7%
8 31
8.5%
10 31
8.5%
12 31
8.5%
5 31
8.5%
1 31
8.5%
3 31
8.5%
9 30
8.2%
4 30
8.2%
11 30
8.2%
Other values (2) 58
15.8%
ValueCountFrequency (%)
1 31
8.5%
2 28
7.7%
3 31
8.5%
4 30
8.2%
5 31
8.5%
6 30
8.2%
7 32
8.7%
8 31
8.5%
9 30
8.2%
10 31
8.5%
ValueCountFrequency (%)
12 31
8.5%
11 30
8.2%
10 31
8.5%
9 30
8.2%
8 31
8.5%
7 32
8.7%
6 30
8.2%
5 31
8.5%
4 30
8.2%
3 31
8.5%

Interactions

2025-08-30T00:20:01.738867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.242024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.907254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.473572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.045096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.726011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.263696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.839085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.443033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.133262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.795074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.303200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.963763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.530963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.104363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.780550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.323079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.901355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.503520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.194503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.849384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.359691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.018685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.588484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.161680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.833822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.382725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.961550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.560933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.253268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.903522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.417631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.075579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.645675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.217869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.887226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.440228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.023925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.731120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.312458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.960057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.474179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.134978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.701863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.275331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.942788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.498783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.084134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.790526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.374046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:02.010176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.615853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.189236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.756878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.329748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.991106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.552284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.145375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.846839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.437444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:02.065526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.675328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.249505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.815348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.388211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.045443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.609578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.205743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.905606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.500746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:02.120723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.736923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.307887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.876210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.447680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.103003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.669691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.267876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.965623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.561855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:02.175117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.794370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.363463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.932600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.610425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.158292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.725151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.325096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.020629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.622007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:02.230241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:56.853863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.422416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:57.992973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:58.670739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.212582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:19:59.785768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:00.388620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.079937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T00:20:01.681442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-30T00:20:04.617600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
closehighlowmonthnum_tradesopenquote_asset_volumetb_base_asset_volumetb_quote_asset_volumevolumeyear
close1.0000.9850.9850.1390.3570.9690.4950.1940.5000.1820.246
high0.9851.0000.9810.1300.4060.9870.5460.2430.5460.2360.286
low0.9850.9811.0000.1530.3020.9840.4330.1190.4340.1120.253
month0.1390.1300.1531.000-0.1410.132-0.005-0.035-0.000-0.0430.983
num_trades0.3570.4060.302-0.1411.0000.3610.8800.8490.8710.8600.101
open0.9690.9870.9840.1320.3611.0000.4880.1750.4830.1740.242
quote_asset_volume0.4950.5460.433-0.0050.8800.4881.0000.9300.9970.9300.104
tb_base_asset_volume0.1940.2430.119-0.0350.8490.1750.9301.0000.9330.9950.000
tb_quote_asset_volume0.5000.5460.434-0.0000.8710.4830.9970.9331.0000.9260.081
volume0.1820.2360.112-0.0430.8600.1740.9300.9950.9261.0000.000
year0.2460.2860.2530.9830.1010.2420.1040.0000.0810.0001.000

Missing values

2025-08-30T00:20:02.311479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-30T00:20:02.387824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

open_timeopenhighlowclosevolumeclose_timequote_asset_volumenum_tradestb_base_asset_volumetb_quote_asset_volumeyearmonth
02024-07-31179.160004185.100006171.610001171.7100073250862.502024-07-31 23:59:59.9995.829395e+0812450801.612652e+062.896163e+0820247
12024-08-01171.699997172.910004157.910004167.3699955523497.502024-08-01 23:59:59.9999.170910e+0818499712.587952e+064.297635e+0820248
22024-08-02167.369995169.429993150.000000152.7700045553018.002024-08-02 23:59:59.9998.809891e+0823963922.668247e+064.232664e+0820248
32024-08-03152.779999154.050003140.000000142.5200045444954.502024-08-03 23:59:59.9998.031329e+0819407552.781021e+064.105722e+0820248
42024-08-04142.529999146.080002131.220001138.3300027037715.502024-08-04 23:59:59.9999.790263e+0823911873.596284e+065.004641e+0820248
52024-08-05138.320007139.369995110.000000129.77999924267558.002024-08-05 23:59:59.9993.003272e+0970925791.176916e+071.457361e+0920248
62024-08-06129.789993149.589996129.399994144.3999948362643.502024-08-06 23:59:59.9991.184670e+0935675994.250034e+066.019862e+0820248
72024-08-07144.399994155.000000142.210007144.7700048372307.502024-08-07 23:59:59.9991.249655e+0936397414.172450e+066.227499e+0820248
82024-08-08144.779999163.699997141.399994163.1300057303134.002024-08-08 23:59:59.9991.137206e+0932359893.683641e+065.739629e+0820248
92024-08-09163.119995163.539993150.720001156.2799994098735.252024-08-09 23:59:59.9996.399081e+0819385412.043792e+063.191060e+0820248
open_timeopenhighlowclosevolumeclose_timequote_asset_volumenum_tradestb_base_asset_volumetb_quote_asset_volumeyearmonth
3562025-07-22195.720001206.300003193.750000205.6999978635627.0002025-07-22 23:59:59.9991.728386e+0952180834.435809e+068.886559e+0820257
3572025-07-23205.699997205.750000183.970001189.3999945548137.5002025-07-23 23:59:59.9991.070982e+0935118502.437732e+064.699754e+0820257
3582025-07-24189.399994191.869995179.210007182.8699955217065.0002025-07-24 23:59:59.9999.715020e+0835205002.440710e+064.550574e+0820257
3592025-07-25182.880005186.850006175.630005186.7599954481310.5002025-07-25 23:59:59.9998.064094e+0832172562.252207e+064.053744e+0820257
3602025-07-26186.750000189.779999184.289993184.8800051773453.8752025-07-26 23:59:59.9993.312358e+0811975968.292621e+051.550039e+0820257
3612025-07-27184.889999190.699997184.529999188.6399992233060.2502025-07-27 23:59:59.9994.186206e+0816192181.136212e+062.130267e+0820257
3622025-07-28188.639999195.259995182.160004183.1100014067872.2502025-07-28 23:59:59.9997.688731e+0827887391.867600e+063.532778e+0820257
3632025-07-29183.100006186.679993178.300003181.4799962541912.0002025-07-29 23:59:59.9994.635967e+0821595171.147045e+062.094628e+0820257
3642025-07-30181.470001182.559998170.289993177.7700043297250.2502025-07-30 23:59:59.9995.859831e+0825024811.509983e+062.682121e+0820257
3652025-07-31177.770004182.699997171.440002172.2200012624841.7502025-07-31 23:59:59.9994.664054e+0819273441.277198e+062.269871e+0820257